[2606.02914] Large AI Models in Dental Healthcare: From General-Purpose Systems to Domain-Specific Foundation Models

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Computer Science > Artificial Intelligence

arXiv:2606.02914 (cs)

[Submitted on 1 Jun 2026 (v1), last revised 3 Jun 2026 (this version, v2)]

Title:Large AI Models in Dental Healthcare: From General-Purpose Systems to Domain-Specific Foundation Models

Authors:Sema Helali, Lina Abu Nada, Sausan Al Kawas, Alaa Abd-Alrazaq, Faleh Tamimi, Rafat Damseh<br>View a PDF of the paper titled Large AI Models in Dental Healthcare: From General-Purpose Systems to Domain-Specific Foundation Models, by Sema Helali and 5 other authors

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Abstract:Background: Oral diseases affect nearly 3.5 billion people worldwide, yet the comparative clinical potential of large-scale AI models in dentistry remains poorly understood. Three distinct model categories have emerged: language-generative models, discriminative vision foundation models, and dental-specific foundation models, with no unified review examining their relationships and collective limitations.

Methods: Following PRISMA-ScR guidelines, we systematically searched four databases (PubMed, Google Scholar, Scopus, arXiv), screened independently by two reviewers. After applying inclusion/exclusion criteria, 97 studies (2020-2026) were included. We propose a two-dimensional classification framework organizing models by architectural paradigm and dental specialization degree.

Results: Language-generative models excel at text-based tasks (clinical reasoning, licensing exams, patient communication) but show inconsistent performance on image-dependent diagnostics. Adapted SAM and CLIP variants achieve strong tooth segmentation and lesion detection results. Dental-specific models (DentVFM, DentVLM, OralGPT) demonstrate strongest performance on complex multimodal tasks. Integrated pipelines consistently outperform single-model approaches. A data asymmetry is observed: dental-specific pretraining concentrates almost entirely in the vision domain, reflecting scarce large-scale dental text corpora.

Conclusions: General-purpose and dental-specific models play complementary roles; the most effective systems combine both within structured pipelines. Safe autonomous deployment requires resolving three persistent barriers: hallucination in generative models, limited annotated dental datasets, and absent standardized clinical evaluation benchmarks.

Subjects:

Artificial Intelligence (cs.AI); Computation and Language (cs.CL)

Cite as:<br>arXiv:2606.02914 [cs.AI]

(or<br>arXiv:2606.02914v2 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2606.02914

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arXiv-issued DOI via DataCite

Submission history<br>From: Sema Helali [view email]<br>[v1]<br>Mon, 1 Jun 2026 21:39:27 UTC (18,026 KB)

[v2]<br>Wed, 3 Jun 2026 03:01:03 UTC (18,026 KB)

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